Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for run_alphafold."""
import json
import os
from absl.testing import absltest
from absl.testing import parameterized
import run_alphafold
import mock
import numpy as np
# Internal import (7716).
TEST_DATA_DIR = 'alphafold/common/testdata/'
class RunAlphafoldTest(parameterized.TestCase):
@parameterized.named_parameters(
('relax', run_alphafold.ModelsToRelax.ALL),
('no_relax', run_alphafold.ModelsToRelax.NONE),
)
def test_end_to_end(self, models_to_relax):
data_pipeline_mock = mock.Mock()
model_runner_mock = mock.Mock()
amber_relaxer_mock = mock.Mock()
data_pipeline_mock.process.return_value = {}
model_runner_mock.process_features.return_value = {
'aatype': np.zeros((12, 10), dtype=np.int32),
'residue_index': np.tile(np.arange(10, dtype=np.int32)[None], (12, 1)),
}
model_runner_mock.predict.return_value = {
'structure_module': {
'final_atom_positions': np.zeros((10, 37, 3)),
'final_atom_mask': np.ones((10, 37)),
},
'predicted_lddt': {
'logits': np.ones((10, 50)),
},
'plddt': np.ones(10) * 42,
'ranking_confidence': 90,
'ptm': np.array(0.),
'aligned_confidence_probs': np.zeros((10, 10, 50)),
'predicted_aligned_error': np.zeros((10, 10)),
'max_predicted_aligned_error': np.array(0.),
}
model_runner_mock.multimer_mode = False
with open(
os.path.join(
absltest.get_default_test_srcdir(), TEST_DATA_DIR, 'glucagon.pdb'
)
) as f:
pdb_string = f.read()
amber_relaxer_mock.process.return_value = (
pdb_string,
None,
[1.0, 0.0, 0.0],
)
out_dir = self.create_tempdir().full_path
fasta_path = os.path.join(out_dir, 'target.fasta')
with open(fasta_path, 'wt') as f:
f.write('>A\nAAAAAAAAAAAAA')
fasta_name = 'test'
run_alphafold.predict_structure(
fasta_path=fasta_path,
fasta_name=fasta_name,
output_dir_base=out_dir,
data_pipeline=data_pipeline_mock,
model_runners={'model1': model_runner_mock},
amber_relaxer=amber_relaxer_mock,
benchmark=False,
random_seed=0,
models_to_relax=models_to_relax,
model_type='Monomer',
)
base_output_files = os.listdir(out_dir)
self.assertIn('target.fasta', base_output_files)
self.assertIn('test', base_output_files)
target_output_files = os.listdir(os.path.join(out_dir, 'test'))
expected_files = [
'confidence_model1.json',
'features.pkl',
'msas',
'pae_model1.json',
'ranked_0.cif',
'ranked_0.pdb',
'ranking_debug.json',
'result_model1.pkl',
'timings.json',
'unrelaxed_model1.cif',
'unrelaxed_model1.pdb',
]
if models_to_relax == run_alphafold.ModelsToRelax.ALL:
expected_files.extend(
['relaxed_model1.cif', 'relaxed_model1.pdb', 'relax_metrics.json']
)
with open(os.path.join(out_dir, 'test', 'relax_metrics.json')) as f:
relax_metrics = json.loads(f.read())
self.assertDictEqual({'model1': {'remaining_violations': [1.0, 0.0, 0.0],
'remaining_violations_count': 1.0}},
relax_metrics)
self.assertCountEqual(expected_files, target_output_files)
# Check that pLDDT is set in the B-factor column.
with open(os.path.join(out_dir, 'test', 'unrelaxed_model1.pdb')) as f:
for line in f:
if line.startswith('ATOM'):
self.assertEqual(line[61:66], '42.00')
if __name__ == '__main__':
absltest.main()